First, the error diffusion method is described.
FIG. 2 is a block diagram illustrating functions for implementing the error diffusion method.
Input image data are sequentially supplied to an adding part 111 by unit of one pixel. Based on a computed operation result, the adding part 111 receives an error diffused to the pixel from a line buffer 112. The adding part 111 adds the error from the line buffer 112 to the input image data so as to generate correction data. The correction data are supplied to a comparison part 113 and an error data generating part 114. The comparison part 113 compares the correction data with a predetermined threshold. If the correction data are larger than the threshold, the dot in question is determined as black. In contrast, if the correction data are smaller than the threshold, the dot is determined as white.
The comparison part 113 supplies an output value to the error data generating part 114. The error data generating part 114 generates error data from the output value and the correction data.
The error data are determined according to the formula; (correction data)−{(output value)×255}. The computed error data are supplied to an error filtering part 115.
The error filtering part 115 computes an error diffused to a neighboring pixel by multiplying the error data by an error filter value predetermined for the neighboring pixel. The computed error is added to a stored error as the corresponding pixel error of the line buffer 112.
In this fashion, the error diffusion method achieves quality improvement on an image. However, the error diffusion method requires a considerable amount of processing time because of many operations such as the addition of the correction data, the subtraction for the error data, the multiplication of the diffusion error, and the addition of the error.
Next, the blue noise mask method is described.
FIG. 3 is a block diagram illustrating functions for implementing the blue noise mask method.
An input image is supplied to a comparison part 121 by unit of one pixel. The comparison part 121 compares a pixel value of the input image with a threshold matrix created according to the blue noise method in advance. The comparison part 121 generates an output value as follows. If the input pixel value is larger than the threshold, the pixel value is determined as “1”. In contrast, if the input pixel value is smaller than the threshold, the pixel value is determined as “0”. Here, the value “1” corresponds to a black dot, and the value “0” corresponds to a white dot.
FIG. 4 is a block diagram illustrating functions for implementing another blue noise mask method.
An input image is supplied to a table reference part 131 by unit of one pixel. The table reference part 131 outputs a “1” pattern or a “0” pattern with reference to a pattern table 132 in which patterns corresponding to input pixel values are registered in advance.
In this blue noise mask method, the input value is just compared with the threshold as shown in FIG. 3, or the reference table created corresponding to input values in advance is just referred to as shown in FIG. 4. Therefore, the blue noise mask method can realize high speed processing.
In the error diffusion method, although a high-quality image is obtained, a large amount of processing time is required. On the other hand, the blue noise mask method has some problems, for instance, in that the image quality is insufficient because of iterative use of patterns.
It is an object of the present invention to provide an image processing method, an image processing apparatus and a recording medium in which the above-mentioned problems are eliminated.
A more specific object of the present invention is to provide an image processing method, an image processing apparatus and a recording medium that achieve high-speed processing.